A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq.

Area of Science:

• Bioinformatics
• Genomics
• Molecular Evolution

Background:

• Analyzing large datasets like whole genomes or transcriptomes presents significant challenges.
• Focusing on specific gene families is an effective strategy to manage and interpret complex biological data.

Purpose of the Study:

• To outline bioinformatic methods for generating phylogenetic trees and quantifying gene expression.
• To demonstrate how to characterize gene families for insights into evolution and function.

Main Methods:

• Phylogenetic tree construction to infer evolutionary relationships and orthology.
• RNA-sequencing (RNA-seq) data analysis to quantify gene expression levels.
• Integration of phylogenetic and expression data for comprehensive gene family characterization.

Main Results:

• Phylogenetic analyses reveal gene evolutionary patterns and interspecies orthology.
• Gene expression quantification highlights expression differences across individuals or tissues.
• Characterization of gene families provides a foundation for further research.

Conclusions:

• This protocol offers a streamlined approach to analyzing complex genomic and transcriptomic data.
• Gene family characterization aids in understanding molecular evolution, gene function conservation, and identifying key genes.
• The methodology serves as a valuable tool for researchers in genomics and evolutionary biology.